Attenuation and velocity estimation using rock physics and neural network methods for calibrating reflection seismograms

AbstractVelocity logs are the most important data used to evaluate rock, fluid, and geotechnical properties of hydrocarbon reservoirs. As a complementary physical property, P-wave attenuation (Q−1) can be used as an indicator of lithology and fluid saturation in oil and gas reservoir characterization. We implemented an inversion self-consistent rock physical model to predict P- and S-wave velocities in two old wells near a new well containing a complete suite of logs at the Waggoner Ranch oil reservoir in northeast Texas. We selected a training data set from the new well to test the algorithm that was subsequently applied to predict velocity data in the two old wells. We used an attenuation log from the new well to perform data analysis via the Gamma test, a mathematically nonparametric nonlinear smooth modeling tool, to choose the best input combination of well logs to train an artificial neural network (NN) for estimating Q−1. Then, the NN was applied to predict attenuation logs in the old wells. The Q−...